Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations12330
Missing cells700
Missing cells (%)0.3%
Duplicate rows59
Duplicate rows (%)0.5%
Total size in memory4.0 MiB
Average record size in memory342.9 B

Variable types

Numeric14
Categorical3
Boolean1

Alerts

Dataset has 59 (0.5%) duplicate rowsDuplicates
Administrative is highly overall correlated with Administrative_DurationHigh correlation
Administrative_Duration is highly overall correlated with AdministrativeHigh correlation
BounceRates is highly overall correlated with ExitRatesHigh correlation
ExitRates is highly overall correlated with BounceRates and 1 other fieldsHigh correlation
Informational is highly overall correlated with Informational_DurationHigh correlation
Informational_Duration is highly overall correlated with InformationalHigh correlation
ProductRelated is highly overall correlated with ExitRates and 1 other fieldsHigh correlation
ProductRelated_Duration is highly overall correlated with ProductRelatedHigh correlation
VisitorType is highly imbalanced (59.9%) Imbalance
Browser has 184 (1.5%) missing values Missing
Region has 246 (2.0%) missing values Missing
Revenue has 147 (1.2%) missing values Missing
Administrative_Duration is highly skewed (γ1 = 28.62347698) Skewed
Administrative has 5704 (46.3%) zeros Zeros
Administrative_Duration has 5898 (47.8%) zeros Zeros
Informational has 9699 (78.7%) zeros Zeros
Informational_Duration has 9925 (80.5%) zeros Zeros
ProductRelated_Duration has 755 (6.1%) zeros Zeros
BounceRates has 5241 (42.5%) zeros Zeros
PageValues has 9600 (77.9%) zeros Zeros
SpecialDay has 10971 (89.0%) zeros Zeros

Reproduction

Analysis started2025-04-11 09:15:17.526212
Analysis finished2025-04-11 09:15:27.516792
Duration9.99 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Administrative
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2381995
Minimum-10
Maximum27
Zeros5704
Zeros (%)46.3%
Negative123
Negative (%)1.0%
Memory size96.5 KiB
2025-04-11T11:15:27.551920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum27
Range37
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.4092693
Coefficient of variation (CV)1.5232196
Kurtosis4.5240775
Mean2.2381995
Median Absolute Deviation (MAD)1
Skewness1.6941611
Sum27597
Variance11.623117
MonotonicityNot monotonic
2025-04-11T11:15:27.607709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 5704
46.3%
1 1341
 
10.9%
2 1101
 
8.9%
3 909
 
7.4%
4 757
 
6.1%
5 574
 
4.7%
6 428
 
3.5%
7 334
 
2.7%
8 285
 
2.3%
9 223
 
1.8%
Other values (27) 674
 
5.5%
ValueCountFrequency (%)
-10 6
 
< 0.1%
-9 15
0.1%
-8 17
0.1%
-7 14
0.1%
-6 16
0.1%
-5 16
0.1%
-4 6
 
< 0.1%
-3 13
0.1%
-2 6
 
< 0.1%
-1 14
0.1%
ValueCountFrequency (%)
27 1
 
< 0.1%
26 1
 
< 0.1%
24 4
 
< 0.1%
23 3
 
< 0.1%
22 4
 
< 0.1%
21 2
 
< 0.1%
20 2
 
< 0.1%
19 6
 
< 0.1%
18 11
0.1%
17 16
0.1%

Administrative_Duration
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct3345
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1269.776
Minimum0
Maximum989493
Zeros5898
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2025-04-11T11:15:27.663527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q393.7875
95-th percentile353.27625
Maximum989493
Range989493
Interquartile range (IQR)93.7875

Descriptive statistics

Standard deviation34071.467
Coefficient of variation (CV)26.832659
Kurtosis817.5146
Mean1269.776
Median Absolute Deviation (MAD)8
Skewness28.623477
Sum15656338
Variance1.1608649 × 109
MonotonicityNot monotonic
2025-04-11T11:15:27.723190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5898
47.8%
4 56
 
0.5%
5 53
 
0.4%
7 45
 
0.4%
11 42
 
0.3%
6 41
 
0.3%
14 36
 
0.3%
9 35
 
0.3%
15 33
 
0.3%
10 32
 
0.3%
Other values (3335) 6059
49.1%
ValueCountFrequency (%)
0 5898
47.8%
1.333333333 1
 
< 0.1%
2 15
 
0.1%
3 26
 
0.2%
3.5 4
 
< 0.1%
4 56
 
0.5%
4.333333333 1
 
< 0.1%
4.5 2
 
< 0.1%
4.75 1
 
< 0.1%
5 53
 
0.4%
ValueCountFrequency (%)
989493 1
< 0.1%
987522 1
< 0.1%
985308 1
< 0.1%
985147 1
< 0.1%
984643 1
< 0.1%
984508 1
< 0.1%
980612 1
< 0.1%
977747 1
< 0.1%
973917 1
< 0.1%
971707 1
< 0.1%

Informational
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50356853
Minimum0
Maximum24
Zeros9699
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2025-04-11T11:15:27.774421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2701564
Coefficient of variation (CV)2.522311
Kurtosis26.932266
Mean0.50356853
Median Absolute Deviation (MAD)0
Skewness4.0364638
Sum6209
Variance1.6132973
MonotonicityNot monotonic
2025-04-11T11:15:27.822327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 9699
78.7%
1 1041
 
8.4%
2 728
 
5.9%
3 380
 
3.1%
4 222
 
1.8%
5 99
 
0.8%
6 78
 
0.6%
7 36
 
0.3%
9 15
 
0.1%
8 14
 
0.1%
Other values (7) 18
 
0.1%
ValueCountFrequency (%)
0 9699
78.7%
1 1041
 
8.4%
2 728
 
5.9%
3 380
 
3.1%
4 222
 
1.8%
5 99
 
0.8%
6 78
 
0.6%
7 36
 
0.3%
8 14
 
0.1%
9 15
 
0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 5
 
< 0.1%
11 1
 
< 0.1%
10 7
 
0.1%
9 15
0.1%
8 14
 
0.1%
7 36
0.3%

Informational_Duration
Real number (ℝ)

High correlation  Zeros 

Distinct1258
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.472398
Minimum0
Maximum2549.375
Zeros9925
Zeros (%)80.5%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2025-04-11T11:15:27.872337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile195
Maximum2549.375
Range2549.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation140.74929
Coefficient of variation (CV)4.0829563
Kurtosis76.316853
Mean34.472398
Median Absolute Deviation (MAD)0
Skewness7.5791847
Sum425044.67
Variance19810.364
MonotonicityNot monotonic
2025-04-11T11:15:27.953294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9925
80.5%
9 33
 
0.3%
7 26
 
0.2%
10 26
 
0.2%
6 26
 
0.2%
12 23
 
0.2%
13 23
 
0.2%
16 22
 
0.2%
8 22
 
0.2%
11 21
 
0.2%
Other values (1248) 2183
 
17.7%
ValueCountFrequency (%)
0 9925
80.5%
1 3
 
< 0.1%
1.5 1
 
< 0.1%
2 11
 
0.1%
2.5 1
 
< 0.1%
3 16
 
0.1%
3.5 1
 
< 0.1%
4 17
 
0.1%
5 18
 
0.1%
5.5 3
 
< 0.1%
ValueCountFrequency (%)
2549.375 1
< 0.1%
2256.916667 1
< 0.1%
2252.033333 1
< 0.1%
2195.3 1
< 0.1%
2166.5 1
< 0.1%
2050.433333 1
< 0.1%
1949.166667 1
< 0.1%
1830.5 1
< 0.1%
1779.166667 1
< 0.1%
1778 1
< 0.1%

ProductRelated
Real number (ℝ)

High correlation 

Distinct311
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.731468
Minimum0
Maximum705
Zeros38
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2025-04-11T11:15:28.022848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median18
Q338
95-th percentile109
Maximum705
Range705
Interquartile range (IQR)31

Descriptive statistics

Standard deviation44.475503
Coefficient of variation (CV)1.4016214
Kurtosis31.211707
Mean31.731468
Median Absolute Deviation (MAD)13
Skewness4.3415164
Sum391249
Variance1978.0704
MonotonicityNot monotonic
2025-04-11T11:15:28.097763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 622
 
5.0%
2 465
 
3.8%
3 458
 
3.7%
4 404
 
3.3%
6 396
 
3.2%
7 391
 
3.2%
5 382
 
3.1%
8 370
 
3.0%
10 330
 
2.7%
9 317
 
2.6%
Other values (301) 8195
66.5%
ValueCountFrequency (%)
0 38
 
0.3%
1 622
5.0%
2 465
3.8%
3 458
3.7%
4 404
3.3%
5 382
3.1%
6 396
3.2%
7 391
3.2%
8 370
3.0%
9 317
2.6%
ValueCountFrequency (%)
705 1
< 0.1%
686 1
< 0.1%
584 1
< 0.1%
534 1
< 0.1%
518 1
< 0.1%
517 1
< 0.1%
501 1
< 0.1%
486 1
< 0.1%
470 1
< 0.1%
449 1
< 0.1%

ProductRelated_Duration
Real number (ℝ)

High correlation  Zeros 

Distinct9551
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1194.7462
Minimum0
Maximum63973.522
Zeros755
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2025-04-11T11:15:28.154945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1184.1375
median598.9369
Q31464.1572
95-th percentile4300.2891
Maximum63973.522
Range63973.522
Interquartile range (IQR)1280.0197

Descriptive statistics

Standard deviation1913.6693
Coefficient of variation (CV)1.6017371
Kurtosis137.17416
Mean1194.7462
Median Absolute Deviation (MAD)500.9369
Skewness7.2632277
Sum14731221
Variance3662130.1
MonotonicityNot monotonic
2025-04-11T11:15:28.209478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 755
 
6.1%
17 21
 
0.2%
11 17
 
0.1%
8 17
 
0.1%
15 16
 
0.1%
12 15
 
0.1%
19 15
 
0.1%
22 15
 
0.1%
13 14
 
0.1%
7 14
 
0.1%
Other values (9541) 11431
92.7%
ValueCountFrequency (%)
0 755
6.1%
0.5 1
 
< 0.1%
1 2
 
< 0.1%
2.333333333 1
 
< 0.1%
2.666666667 1
 
< 0.1%
3 5
 
< 0.1%
4 10
 
0.1%
5 13
 
0.1%
5.333333333 1
 
< 0.1%
6 5
 
< 0.1%
ValueCountFrequency (%)
63973.52223 1
< 0.1%
43171.23338 1
< 0.1%
29970.46597 1
< 0.1%
27009.85943 1
< 0.1%
24844.1562 1
< 0.1%
23888.81 1
< 0.1%
23342.08205 1
< 0.1%
23050.10414 1
< 0.1%
21857.04648 1
< 0.1%
21672.24425 1
< 0.1%

BounceRates
Real number (ℝ)

High correlation  Zeros 

Distinct2408
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.044239196
Minimum-0.49867988
Maximum2.0989518
Zeros5241
Zeros (%)42.5%
Negative363
Negative (%)2.9%
Memory size96.5 KiB
2025-04-11T11:15:28.265815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.49867988
5-th percentile0
Q10
median0.0028955565
Q30.018029095
95-th percentile0.2
Maximum2.0989518
Range2.5976317
Interquartile range (IQR)0.018029095

Descriptive statistics

Standard deviation0.23973485
Coefficient of variation (CV)5.4190599
Kurtosis40.773001
Mean0.044239196
Median Absolute Deviation (MAD)0.0028955565
Skewness6.0191017
Sum545.46929
Variance0.0574728
MonotonicityNot monotonic
2025-04-11T11:15:28.321706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5241
42.5%
0.2 653
 
5.3%
0.066666667 126
 
1.0%
0.028571429 113
 
0.9%
0.05 109
 
0.9%
0.033333333 99
 
0.8%
0.025 98
 
0.8%
0.016666667 95
 
0.8%
0.04 94
 
0.8%
0.1 92
 
0.7%
Other values (2398) 5610
45.5%
ValueCountFrequency (%)
-0.4986798793 1
< 0.1%
-0.4983312412 1
< 0.1%
-0.4978888815 1
< 0.1%
-0.4970673082 1
< 0.1%
-0.4952831691 1
< 0.1%
-0.4943992066 1
< 0.1%
-0.4941998756 1
< 0.1%
-0.4929793163 1
< 0.1%
-0.4927393889 1
< 0.1%
-0.4920970926 1
< 0.1%
ValueCountFrequency (%)
2.098951828 1
< 0.1%
2.088658098 1
< 0.1%
2.086473382 1
< 0.1%
2.083101267 1
< 0.1%
2.075386901 1
< 0.1%
2.07369663 1
< 0.1%
2.071993849 1
< 0.1%
2.068164836 1
< 0.1%
2.067674648 1
< 0.1%
2.066496026 1
< 0.1%

ExitRates
Real number (ℝ)

High correlation 

Distinct4777
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.043072798
Minimum0
Maximum0.2
Zeros76
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2025-04-11T11:15:28.377362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004567568
Q10.014285714
median0.025156403
Q30.05
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.035714286

Descriptive statistics

Standard deviation0.048596541
Coefficient of variation (CV)1.128242
Kurtosis4.0170346
Mean0.043072798
Median Absolute Deviation (MAD)0.01417258
Skewness2.148789
Sum531.0876
Variance0.0023616238
MonotonicityNot monotonic
2025-04-11T11:15:28.437460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 710
 
5.8%
0.1 338
 
2.7%
0.05 329
 
2.7%
0.033333333 291
 
2.4%
0.066666667 267
 
2.2%
0.025 224
 
1.8%
0.04 214
 
1.7%
0.016666667 181
 
1.5%
0.02 167
 
1.4%
0.022222222 152
 
1.2%
Other values (4767) 9457
76.7%
ValueCountFrequency (%)
0 76
0.6%
0.000175593 1
 
< 0.1%
0.000250438 1
 
< 0.1%
0.000262123 1
 
< 0.1%
0.000263158 1
 
< 0.1%
0.000292398 1
 
< 0.1%
0.000409836 1
 
< 0.1%
0.000446429 1
 
< 0.1%
0.000468384 1
 
< 0.1%
0.000480769 1
 
< 0.1%
ValueCountFrequency (%)
0.2 710
5.8%
0.192307692 1
 
< 0.1%
0.188888889 2
 
< 0.1%
0.186666667 4
 
< 0.1%
0.183333333 2
 
< 0.1%
0.181818182 1
 
< 0.1%
0.18034188 1
 
< 0.1%
0.18 3
 
< 0.1%
0.177777778 5
 
< 0.1%
0.175 6
 
< 0.1%

PageValues
Real number (ℝ)

Zeros 

Distinct2704
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8892579
Minimum0
Maximum361.76374
Zeros9600
Zeros (%)77.9%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2025-04-11T11:15:28.497207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile38.160528
Maximum361.76374
Range361.76374
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.568437
Coefficient of variation (CV)3.1529332
Kurtosis65.635694
Mean5.8892579
Median Absolute Deviation (MAD)0
Skewness6.3829642
Sum72614.549
Variance344.78684
MonotonicityNot monotonic
2025-04-11T11:15:28.555069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9600
77.9%
53.988 6
 
< 0.1%
42.29306752 3
 
< 0.1%
59.988 2
 
< 0.1%
16.1585582 2
 
< 0.1%
44.89345937 2
 
< 0.1%
14.1273698 2
 
< 0.1%
34.03997536 2
 
< 0.1%
10.99901844 2
 
< 0.1%
58.9241766 2
 
< 0.1%
Other values (2694) 2707
 
22.0%
ValueCountFrequency (%)
0 9600
77.9%
0.038034542 1
 
< 0.1%
0.067049546 1
 
< 0.1%
0.093546949 1
 
< 0.1%
0.098621403 1
 
< 0.1%
0.120699914 1
 
< 0.1%
0.129676893 1
 
< 0.1%
0.131837013 1
 
< 0.1%
0.139200623 1
 
< 0.1%
0.150650498 1
 
< 0.1%
ValueCountFrequency (%)
361.7637419 1
< 0.1%
360.9533839 1
< 0.1%
287.9537928 1
< 0.1%
270.7846931 1
< 0.1%
261.4912857 1
< 0.1%
258.5498732 1
< 0.1%
255.5691579 1
< 0.1%
254.6071579 1
< 0.1%
246.7585902 1
< 0.1%
239.98 1
< 0.1%

SpecialDay
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing123
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.061259933
Minimum0
Maximum1
Zeros10971
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2025-04-11T11:15:28.605060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.19864976
Coefficient of variation (CV)3.2427355
Kurtosis9.9567589
Mean0.061259933
Median Absolute Deviation (MAD)0
Skewness3.3087006
Sum747.8
Variance0.039461726
MonotonicityNot monotonic
2025-04-11T11:15:28.648248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 10971
89.0%
0.6 346
 
2.8%
0.8 321
 
2.6%
0.4 240
 
1.9%
0.2 177
 
1.4%
1 152
 
1.2%
(Missing) 123
 
1.0%
ValueCountFrequency (%)
0 10971
89.0%
0.2 177
 
1.4%
0.4 240
 
1.9%
0.6 346
 
2.8%
0.8 321
 
2.6%
1 152
 
1.2%
ValueCountFrequency (%)
1 152
 
1.2%
0.8 321
 
2.6%
0.6 346
 
2.8%
0.4 240
 
1.9%
0.2 177
 
1.4%
0 10971
89.0%

Month
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size723.1 KiB
May
3196 
Nov
2998 
Mar
1907 
Dec
1727 
Oct
549 
Other values (7)
1953 

Length

Max length4
Median length3
Mean length3.0442011
Min length3

Characters and Unicode

Total characters37535
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFeb
2nd rowFeb
3rd rowFeb
4th rowFeb
5th rowFeb

Common Values

ValueCountFrequency (%)
May 3196
25.9%
Nov 2998
24.3%
Mar 1907
15.5%
Dec 1727
14.0%
Oct 549
 
4.5%
Aug 433
 
3.5%
Jul 432
 
3.5%
Sep 359
 
2.9%
June 288
 
2.3%
Feb 184
 
1.5%
Other values (2) 257
 
2.1%

Length

2025-04-11T11:15:28.695225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may 3196
25.9%
nov 2998
24.3%
mar 1907
15.5%
dec 1727
14.0%
oct 549
 
4.5%
aug 433
 
3.5%
jul 432
 
3.5%
sep 359
 
2.9%
june 288
 
2.3%
feb 184
 
1.5%
Other values (2) 257
 
2.1%

Most occurring characters

ValueCountFrequency (%)
M 5103
13.6%
a 5103
13.6%
y 3196
8.5%
N 2998
8.0%
o 2998
8.0%
v 2998
8.0%
e 2647
7.1%
c 2444
 
6.5%
r 2075
 
5.5%
D 1727
 
4.6%
Other values (13) 6246
16.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25205
67.2%
Uppercase Letter 12330
32.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5103
20.2%
y 3196
12.7%
o 2998
11.9%
v 2998
11.9%
e 2647
10.5%
c 2444
9.7%
r 2075
8.2%
u 1321
 
5.2%
t 638
 
2.5%
p 448
 
1.8%
Other values (4) 1337
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
M 5103
41.4%
N 2998
24.3%
D 1727
 
14.0%
J 720
 
5.8%
O 549
 
4.5%
S 448
 
3.6%
A 433
 
3.5%
F 184
 
1.5%
T 168
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 37535
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 5103
13.6%
a 5103
13.6%
y 3196
8.5%
N 2998
8.0%
o 2998
8.0%
v 2998
8.0%
e 2647
7.1%
c 2444
 
6.5%
r 2075
 
5.5%
D 1727
 
4.6%
Other values (13) 6246
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 5103
13.6%
a 5103
13.6%
y 3196
8.5%
N 2998
8.0%
o 2998
8.0%
v 2998
8.0%
e 2647
7.1%
c 2444
 
6.5%
r 2075
 
5.5%
D 1727
 
4.6%
Other values (13) 6246
16.6%

OperatingSystems
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1240065
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2025-04-11T11:15:28.734375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91132483
Coefficient of variation (CV)0.42905934
Kurtosis10.456843
Mean2.1240065
Median Absolute Deviation (MAD)0
Skewness2.066285
Sum26189
Variance0.83051294
MonotonicityNot monotonic
2025-04-11T11:15:28.777108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 6601
53.5%
1 2585
 
21.0%
3 2555
 
20.7%
4 478
 
3.9%
8 79
 
0.6%
6 19
 
0.2%
7 7
 
0.1%
5 6
 
< 0.1%
ValueCountFrequency (%)
1 2585
 
21.0%
2 6601
53.5%
3 2555
 
20.7%
4 478
 
3.9%
5 6
 
< 0.1%
6 19
 
0.2%
7 7
 
0.1%
8 79
 
0.6%
ValueCountFrequency (%)
8 79
 
0.6%
7 7
 
0.1%
6 19
 
0.2%
5 6
 
< 0.1%
4 478
 
3.9%
3 2555
 
20.7%
2 6601
53.5%
1 2585
 
21.0%

Browser
Real number (ℝ)

Missing 

Distinct13
Distinct (%)0.1%
Missing184
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean2.3602832
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2025-04-11T11:15:28.819778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7220006
Coefficient of variation (CV)0.72957373
Kurtosis12.682995
Mean2.3602832
Median Absolute Deviation (MAD)0
Skewness3.2352112
Sum28668
Variance2.9652862
MonotonicityNot monotonic
2025-04-11T11:15:28.864927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 7835
63.5%
1 2424
 
19.7%
4 725
 
5.9%
5 465
 
3.8%
6 171
 
1.4%
10 160
 
1.3%
8 135
 
1.1%
3 104
 
0.8%
13 61
 
0.5%
7 49
 
0.4%
Other values (3) 17
 
0.1%
(Missing) 184
 
1.5%
ValueCountFrequency (%)
1 2424
 
19.7%
2 7835
63.5%
3 104
 
0.8%
4 725
 
5.9%
5 465
 
3.8%
6 171
 
1.4%
7 49
 
0.4%
8 135
 
1.1%
9 1
 
< 0.1%
10 160
 
1.3%
ValueCountFrequency (%)
13 61
 
0.5%
12 10
 
0.1%
11 6
 
< 0.1%
10 160
 
1.3%
9 1
 
< 0.1%
8 135
 
1.1%
7 49
 
0.4%
6 171
 
1.4%
5 465
3.8%
4 725
5.9%

Region
Real number (ℝ)

Missing 

Distinct9
Distinct (%)0.1%
Missing246
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.1456471
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2025-04-11T11:15:28.903299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4024399
Coefficient of variation (CV)0.7637347
Kurtosis-0.14821254
Mean3.1456471
Median Absolute Deviation (MAD)2
Skewness0.98434139
Sum38012
Variance5.7717174
MonotonicityNot monotonic
2025-04-11T11:15:28.944865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4695
38.1%
3 2345
19.0%
4 1157
 
9.4%
2 1113
 
9.0%
6 787
 
6.4%
7 743
 
6.0%
9 501
 
4.1%
8 427
 
3.5%
5 316
 
2.6%
(Missing) 246
 
2.0%
ValueCountFrequency (%)
1 4695
38.1%
2 1113
 
9.0%
3 2345
19.0%
4 1157
 
9.4%
5 316
 
2.6%
6 787
 
6.4%
7 743
 
6.0%
8 427
 
3.5%
9 501
 
4.1%
ValueCountFrequency (%)
9 501
 
4.1%
8 427
 
3.5%
7 743
 
6.0%
6 787
 
6.4%
5 316
 
2.6%
4 1157
 
9.4%
3 2345
19.0%
2 1113
 
9.0%
1 4695
38.1%

TrafficType
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0695864
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2025-04-11T11:15:28.990901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0251692
Coefficient of variation (CV)0.98908557
Kurtosis3.4797106
Mean4.0695864
Median Absolute Deviation (MAD)1
Skewness1.9629867
Sum50178
Variance16.201987
MonotonicityNot monotonic
2025-04-11T11:15:29.038433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 3913
31.7%
1 2451
19.9%
3 2052
16.6%
4 1069
 
8.7%
13 738
 
6.0%
10 450
 
3.6%
6 444
 
3.6%
8 343
 
2.8%
5 260
 
2.1%
11 247
 
2.0%
Other values (10) 363
 
2.9%
ValueCountFrequency (%)
1 2451
19.9%
2 3913
31.7%
3 2052
16.6%
4 1069
 
8.7%
5 260
 
2.1%
6 444
 
3.6%
7 40
 
0.3%
8 343
 
2.8%
9 42
 
0.3%
10 450
 
3.6%
ValueCountFrequency (%)
20 198
 
1.6%
19 17
 
0.1%
18 10
 
0.1%
17 1
 
< 0.1%
16 3
 
< 0.1%
15 38
 
0.3%
14 13
 
0.1%
13 738
6.0%
12 1
 
< 0.1%
11 247
 
2.0%

VisitorType
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size880.2 KiB
Returning_Visitor
10551 
New_Visitor
1694 
Other
 
85

Length

Max length17
Median length17
Mean length16.092944
Min length5

Characters and Unicode

Total characters198426
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReturning_Visitor
2nd rowReturning_Visitor
3rd rowReturning_Visitor
4th rowReturning_Visitor
5th rowReturning_Visitor

Common Values

ValueCountFrequency (%)
Returning_Visitor 10551
85.6%
New_Visitor 1694
 
13.7%
Other 85
 
0.7%

Length

2025-04-11T11:15:29.093007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T11:15:29.136045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
returning_visitor 10551
85.6%
new_visitor 1694
 
13.7%
other 85
 
0.7%

Most occurring characters

ValueCountFrequency (%)
i 35041
17.7%
t 22881
11.5%
r 22881
11.5%
n 21102
10.6%
e 12330
 
6.2%
_ 12245
 
6.2%
V 12245
 
6.2%
s 12245
 
6.2%
o 12245
 
6.2%
R 10551
 
5.3%
Other values (6) 24660
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 161606
81.4%
Uppercase Letter 24575
 
12.4%
Connector Punctuation 12245
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 35041
21.7%
t 22881
14.2%
r 22881
14.2%
n 21102
13.1%
e 12330
 
7.6%
s 12245
 
7.6%
o 12245
 
7.6%
u 10551
 
6.5%
g 10551
 
6.5%
w 1694
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
V 12245
49.8%
R 10551
42.9%
N 1694
 
6.9%
O 85
 
0.3%
Connector Punctuation
ValueCountFrequency (%)
_ 12245
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 186181
93.8%
Common 12245
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 35041
18.8%
t 22881
12.3%
r 22881
12.3%
n 21102
11.3%
e 12330
 
6.6%
V 12245
 
6.6%
s 12245
 
6.6%
o 12245
 
6.6%
R 10551
 
5.7%
u 10551
 
5.7%
Other values (5) 14109
7.6%
Common
ValueCountFrequency (%)
_ 12245
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 35041
17.7%
t 22881
11.5%
r 22881
11.5%
n 21102
10.6%
e 12330
 
6.2%
_ 12245
 
6.2%
V 12245
 
6.2%
s 12245
 
6.2%
o 12245
 
6.2%
R 10551
 
5.3%
Other values (6) 24660
12.4%

Weekend
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size744.6 KiB
False
9273 
True
2868 
Name:Zara
 
189

Length

Max length9
Median length5
Mean length4.8287105
Min length4

Characters and Unicode

Total characters59538
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowFalse
3rd rowFalse
4th rowFalse
5th rowTrue

Common Values

ValueCountFrequency (%)
False 9273
75.2%
True 2868
 
23.3%
Name:Zara 189
 
1.5%

Length

2025-04-11T11:15:29.185650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T11:15:29.228308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
false 9273
75.2%
true 2868
 
23.3%
name:zara 189
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 12330
20.7%
a 9840
16.5%
F 9273
15.6%
l 9273
15.6%
s 9273
15.6%
r 3057
 
5.1%
T 2868
 
4.8%
u 2868
 
4.8%
N 189
 
0.3%
m 189
 
0.3%
Other values (2) 378
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46830
78.7%
Uppercase Letter 12519
 
21.0%
Other Punctuation 189
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12330
26.3%
a 9840
21.0%
l 9273
19.8%
s 9273
19.8%
r 3057
 
6.5%
u 2868
 
6.1%
m 189
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
F 9273
74.1%
T 2868
 
22.9%
N 189
 
1.5%
Z 189
 
1.5%
Other Punctuation
ValueCountFrequency (%)
: 189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59349
99.7%
Common 189
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12330
20.8%
a 9840
16.6%
F 9273
15.6%
l 9273
15.6%
s 9273
15.6%
r 3057
 
5.2%
T 2868
 
4.8%
u 2868
 
4.8%
N 189
 
0.3%
m 189
 
0.3%
Common
ValueCountFrequency (%)
: 189
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12330
20.7%
a 9840
16.5%
F 9273
15.6%
l 9273
15.6%
s 9273
15.6%
r 3057
 
5.1%
T 2868
 
4.8%
u 2868
 
4.8%
N 189
 
0.3%
m 189
 
0.3%
Other values (2) 378
 
0.6%

Revenue
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing147
Missing (%)1.2%
Memory size433.0 KiB
False
10303 
True
1880 
(Missing)
 
147
ValueCountFrequency (%)
False 10303
83.6%
True 1880
 
15.2%
(Missing) 147
 
1.2%
2025-04-11T11:15:29.269257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2025-04-11T11:15:26.508934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.011404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.083755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.659697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.313605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.879102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.456739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.175920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.746619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.348559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.999707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.778317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.352129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.910080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.552268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.062423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.126567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.699598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.355046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.921018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.499780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.219191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.790410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.390355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.046806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.819731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.392062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.954153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.593772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.108493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.166409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.738507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.395143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.962220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.541051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.260011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.832661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.430902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.248578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.861444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.430686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.994455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.633106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.150255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.204780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.775590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.433842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.001091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.580242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.298303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.874305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.470396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.289089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.899888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.468246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.034131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.676206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.192788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.244713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.813716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.471061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.040995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.620420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.338576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.915855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.522071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.329462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.941032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.506161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.073348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.891265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.236973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.286967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.854304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.513646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.081612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.663037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.380145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.959670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.576818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.371014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.982771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.546765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.116888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.934480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.746810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.329058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.895921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.554164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.124145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.704265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.422436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.002886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.618876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.412648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.024093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.587193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.158535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.974980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.787080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.367722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.934005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.593505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.163204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.744527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.460349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.044535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.660304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.453194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.063295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.625315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.198395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:27.020864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.832113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.412297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.976921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.636188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.208315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.789779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.503664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.088644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.711270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.495881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.107148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.671525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.241671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:27.065716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.874775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.453693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.018031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.679269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.249905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.947312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.546634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.133792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.761276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.539161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.148720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.714625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.284861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:27.106110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.915023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.493157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.055564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.717476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.289823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.987570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.584635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.174860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.811839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.607118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.187896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.753416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.324293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:27.149028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.956495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.535177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.192455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.758436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.331786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.030905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.625961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.219284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.858688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.658470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.228345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.793093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.368375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:27.189358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:18.996563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.574627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.229809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.796876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.370382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.070652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.663914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.259501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.906328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.696469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.267063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.829813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.415871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:27.233161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.039952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:19.617171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.271350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:20.837629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:21.413971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.113856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:22.705626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.304867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:23.954343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:24.737163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.308928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:25.870155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-11T11:15:26.465061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-11T11:15:29.309304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AdministrativeAdministrative_DurationBounceRatesBrowserExitRatesInformationalInformational_DurationMonthOperatingSystemsPageValuesProductRelatedProductRelated_DurationRegionRevenueSpecialDayTrafficTypeVisitorTypeWeekend
Administrative1.0000.920-0.126-0.009-0.4250.3610.3540.059-0.0010.3240.4520.4150.0110.138-0.125-0.0130.1020.037
Administrative_Duration0.9201.000-0.133-0.022-0.4370.3560.3510.021-0.0080.3160.4290.4120.0180.007-0.131-0.0160.0350.000
BounceRates-0.126-0.1331.000-0.0460.5330.0090.0020.0500.051-0.104-0.037-0.067-0.0110.1620.1270.0190.1140.027
Browser-0.009-0.022-0.0461.000-0.017-0.020-0.0130.0600.3750.0250.0440.0450.0570.0370.0180.0000.4750.039
ExitRates-0.425-0.4370.533-0.0171.000-0.186-0.2000.0580.022-0.308-0.519-0.477-0.0030.2450.1520.0220.1840.044
Informational0.3610.3560.009-0.020-0.1861.0000.9510.0160.0000.2190.3690.368-0.0230.078-0.054-0.0290.0280.014
Informational_Duration0.3540.3510.002-0.013-0.2000.9511.0000.0000.0030.2240.3610.363-0.0150.067-0.054-0.0260.0080.000
Month0.0590.0210.0500.0600.0580.0160.0001.0000.0580.0180.0680.0460.0370.1750.2360.1600.1370.041
OperatingSystems-0.001-0.0080.0510.3750.0220.0000.0030.0581.000-0.0120.0210.0230.0270.0740.0220.0800.4650.086
PageValues0.3240.316-0.1040.025-0.3080.2190.2240.018-0.0121.0000.3420.3600.0020.413-0.070-0.0180.1100.048
ProductRelated0.4520.429-0.0370.044-0.5190.3690.3610.0680.0210.3421.0000.883-0.0200.127-0.022-0.0700.0790.000
ProductRelated_Duration0.4150.412-0.0670.045-0.4770.3680.3630.0460.0230.3600.8831.000-0.0080.069-0.049-0.0730.0350.000
Region0.0110.018-0.0110.057-0.003-0.023-0.0150.0370.0270.002-0.020-0.0081.0000.007-0.016-0.0050.1860.016
Revenue0.1380.0070.1620.0370.2450.0780.0670.1750.0740.4130.1270.0690.0071.0000.0860.1210.1050.031
SpecialDay-0.125-0.1310.1270.0180.152-0.054-0.0540.2360.022-0.070-0.022-0.049-0.0160.0861.0000.1100.0650.183
TrafficType-0.013-0.0160.0190.0000.022-0.029-0.0260.1600.080-0.018-0.070-0.073-0.0050.1210.1101.0000.3160.064
VisitorType0.1020.0350.1140.4750.1840.0280.0080.1370.4650.1100.0790.0350.1860.1050.0650.3161.0000.038
Weekend0.0370.0000.0270.0390.0440.0140.0000.0410.0860.0480.0000.0000.0160.0310.1830.0640.0381.000

Missing values

2025-04-11T11:15:27.297181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-11T11:15:27.405869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-11T11:15:27.486029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
000.000.010.0000000.2000000.2000000.00.0Feb1NaN1.01Returning_VisitorFalseFalse
100.000.0264.0000000.0000000.1000000.00.0Feb22.0NaN2Returning_VisitorFalseFalse
200.000.010.0000000.2000000.2000000.00.0Feb41.0NaN3Returning_VisitorFalseFalse
300.000.022.6666670.0500000.1400000.00.0Feb32.02.04Returning_VisitorFalseFalse
400.000.010627.5000000.0200000.0500000.00.0Feb33.01.04Returning_VisitorTrueFalse
500.000.019154.2166670.0157890.0245610.00.0Feb22.01.03Returning_VisitorFalseFalse
600.000.010.0000000.2000000.2000000.00.4Feb24.03.03Returning_VisitorFalseFalse
710.000.000.0000000.2000000.2000000.00.0Feb12.01.05Returning_VisitorTrueFalse
800.000.0237.0000000.0000000.1000000.00.8Feb22.02.03Returning_VisitorFalseFalse
900.000.03738.0000000.0000000.0222220.00.4Feb24.01.02Returning_VisitorFalseFalse
AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
1232000.0000.08143.5833330.0142860.0500000.0000000.0Nov22.03.01Returning_VisitorFalseFalse
1232100.0000.060.0000000.2000000.2000000.0000000.0Nov18.04.01Returning_VisitorFalseFalse
12322676.2500.0221075.2500000.0000000.0041670.0000000.0Dec22.04.02Returning_VisitorFalseFalse
12323264.7500.0441157.9761900.0000000.0139530.0000000.0Nov22.01.010Returning_VisitorFalseFalse
1232400.0010.016503.0000000.0000000.0376470.0000000.0Nov22.01.01Returning_VisitorFalseFalse
123253145.0000.0531783.7916670.0071430.02903112.2417170.0Dec46.0NaN1Returning_VisitorTrueFalse
1232600.0000.05465.7500000.0000000.0213330.0000000.0Nov32.01.08Returning_VisitorTrueFalse
1232700.0000.06184.2500000.0833330.0866670.0000000.0Nov32.01.013Returning_VisitorTrueFalse
12328475.0000.015346.0000000.0000000.0210530.0000000.0Nov22.03.011Returning_VisitorFalseFalse
1232900.0000.0321.2500000.0000000.0666670.0000000.0Nov32.01.02New_VisitorTrueFalse

Duplicate rows

Most frequently occurring

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue# duplicates
2300.000.010.00.20.20.00.0Mar22.01.01Returning_VisitorFalseFalse12
3800.000.010.00.20.20.00.0May22.01.03Returning_VisitorFalseFalse7
1200.000.010.00.20.20.00.0Dec813.09.020OtherFalseFalse5
3300.000.010.00.20.20.00.0May11.01.03Returning_VisitorFalseFalse5
3100.000.010.00.20.20.00.0Mar32.03.01Returning_VisitorFalseFalse4
000.000.010.00.20.20.00.0Dec11.01.01Returning_VisitorTrueFalse3
300.000.010.00.20.20.00.0Dec11.04.01Returning_VisitorTrueFalse3
500.000.010.00.20.20.00.0Dec22.01.01Returning_VisitorFalseFalse3
2100.000.010.00.20.20.00.0Mar11.03.03Returning_VisitorFalseFalse3
2200.000.010.00.20.20.00.0Mar11.08.01Returning_VisitorFalseFalse3